



'''simple docstring'''

import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock

import torch
from accelerate.utils import write_basic_config

from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.utils import is_apex_available


logging.basicConfig(level=logging.DEBUG)

SCREAMING_SNAKE_CASE__     :						List[str]	=  logging.getLogger()






def 				a  (     )       ->	str:
		snake_case__	     =argparse.ArgumentParser()
		parser.add_argument('-f'			)
		snake_case__	     =parser.parse_args()
		return args.f






def 				a  (     UpperCamelCase_    :    Optional[int]			)       ->	Optional[int]:
		snake_case__	     ={}
		snake_case__	     =os.path.join(UpperCamelCase_      ,					'all_results.json'			)
		if os.path.exists(UpperCamelCase_			):
				with open(UpperCamelCase_      ,					'r'			) as f:
						snake_case__	     =json.load(UpperCamelCase_			)
		else:
				raise ValueError(f"""can't find {path}"""			)
		return results






def 				a  (     )       ->	List[Any]:
		snake_case__	     =torch.cuda.is_available() and torch_device == 'cuda'
		return is_using_cuda and is_apex_available()


SCREAMING_SNAKE_CASE__     :						Any	=  logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)


class 							a__( snake_case__     ):






			@classmethod
			def 						_lowercase  (     cls						)							->							List[Any]:
					# Write Accelerate config, will pick up on CPU, GPU, and multi-GPU
					snake_case__	     =tempfile.mkdtemp()
					snake_case__	     =os.path.join(cls.tmpdir					,   'default_config.yml'						)
					write_basic_config(save_location=cls.configPath						)
					snake_case__	     =['accelerate', 'launch', '--config_file', cls.configPath]






			@classmethod
			def 						_lowercase  (     cls						)							->							Tuple:
					shutil.rmtree(cls.tmpdir						)






			@mock.patch.dict(os.environ					,   {'WANDB_MODE': 'offline'}						)
			def 						_lowercase  (     self						)							->							List[Any]:
					snake_case__	     =self.get_auto_remove_tmp_dir()
					snake_case__	     =f"""
            {self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --seed=42
            --checkpointing_steps epoch
            --with_tracking
        """.split()

					if is_cuda_and_apex_available():
							testargs.append('--fp16'						)

					run_command(self._launch_args + testargs						)
					snake_case__	     =get_results(_UpperCAmelCase						)
					self.assertGreaterEqual(result['eval_accuracy']					,   0.75						)
					self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase					,   'epoch_0'						)						)						)
					self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase					,   'glue_no_trainer'						)						)						)






			@mock.patch.dict(os.environ					,   {'WANDB_MODE': 'offline'}						)
			def 						_lowercase  (     self						)							->							List[Any]:
					snake_case__	     =self.get_auto_remove_tmp_dir()
					snake_case__	     =f"""
            {self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --block_size 128
            --per_device_train_batch_size 5
            --per_device_eval_batch_size 5
            --num_train_epochs 2
            --output_dir {tmp_dir}
            --checkpointing_steps epoch
            --with_tracking
        """.split()

					if torch.cuda.device_count() > 1:
							# Skipping because there are not enough batches to train the model + would need a drop_last to work.
							return

					run_command(self._launch_args + testargs						)
					snake_case__	     =get_results(_UpperCAmelCase						)
					self.assertLess(result['perplexity']					,   100						)
					self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase					,   'epoch_0'						)						)						)
					self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase					,   'clm_no_trainer'						)						)						)






			@mock.patch.dict(os.environ					,   {'WANDB_MODE': 'offline'}						)
			def 						_lowercase  (     self						)							->							Optional[Any]:
					snake_case__	     =self.get_auto_remove_tmp_dir()
					snake_case__	     =f"""
            {self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --output_dir {tmp_dir}
            --num_train_epochs=1
            --checkpointing_steps epoch
            --with_tracking
        """.split()

					run_command(self._launch_args + testargs						)
					snake_case__	     =get_results(_UpperCAmelCase						)
					self.assertLess(result['perplexity']					,   42						)
					self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase					,   'epoch_0'						)						)						)
					self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase					,   'mlm_no_trainer'						)						)						)






			@mock.patch.dict(os.environ					,   {'WANDB_MODE': 'offline'}						)
			def 						_lowercase  (     self						)							->							List[str]:
					# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
					snake_case__	     =7 if get_gpu_count() > 1 else 2

					snake_case__	     =self.get_auto_remove_tmp_dir()
					snake_case__	     =f"""
            {self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/conll/sample.json
            --validation_file tests/fixtures/tests_samples/conll/sample.json
            --output_dir {tmp_dir}
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
            --checkpointing_steps epoch
            --with_tracking
        """.split()

					run_command(self._launch_args + testargs						)
					snake_case__	     =get_results(_UpperCAmelCase						)
					self.assertGreaterEqual(result['eval_accuracy']					,   0.75						)
					self.assertLess(result['train_loss']					,   0.5						)
					self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase					,   'epoch_0'						)						)						)
					self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase					,   'ner_no_trainer'						)						)						)






			@unittest.skip(reason='Fix me @muellerzr'						)
			@mock.patch.dict(os.environ					,   {'WANDB_MODE': 'offline'}						)
			def 						_lowercase  (     self						)							->							Union[str, Any]:
					snake_case__	     =self.get_auto_remove_tmp_dir()
					snake_case__	     =f"""
            {self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py
            --model_name_or_path bert-base-uncased
            --version_2_with_negative
            --train_file tests/fixtures/tests_samples/SQUAD/sample.json
            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json
            --output_dir {tmp_dir}
            --seed=42
            --max_train_steps=10
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
            --with_tracking
        """.split()

					run_command(self._launch_args + testargs						)
					snake_case__	     =get_results(_UpperCAmelCase						)
					# Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
					self.assertGreaterEqual(result['eval_f1']					,   28						)
					self.assertGreaterEqual(result['eval_exact']					,   28						)
					self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase					,   'epoch_0'						)						)						)
					self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase					,   'qa_no_trainer'						)						)						)






			@mock.patch.dict(os.environ					,   {'WANDB_MODE': 'offline'}						)
			def 						_lowercase  (     self						)							->							int:
					snake_case__	     =self.get_auto_remove_tmp_dir()
					snake_case__	     =f"""
            {self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/swag/sample.json
            --validation_file tests/fixtures/tests_samples/swag/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=20
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --with_tracking
        """.split()

					run_command(self._launch_args + testargs						)
					snake_case__	     =get_results(_UpperCAmelCase						)
					self.assertGreaterEqual(result['eval_accuracy']					,   0.8						)
					self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase					,   'swag_no_trainer'						)						)						)






			@slow
			@mock.patch.dict(os.environ					,   {'WANDB_MODE': 'offline'}						)
			def 						_lowercase  (     self						)							->							Tuple:
					snake_case__	     =self.get_auto_remove_tmp_dir()
					snake_case__	     =f"""
            {self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py
            --model_name_or_path t5-small
            --train_file tests/fixtures/tests_samples/xsum/sample.json
            --validation_file tests/fixtures/tests_samples/xsum/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=50
            --num_warmup_steps=8
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
            --with_tracking
        """.split()

					run_command(self._launch_args + testargs						)
					snake_case__	     =get_results(_UpperCAmelCase						)
					self.assertGreaterEqual(result['eval_rouge1']					,   10						)
					self.assertGreaterEqual(result['eval_rouge2']					,   2						)
					self.assertGreaterEqual(result['eval_rougeL']					,   7						)
					self.assertGreaterEqual(result['eval_rougeLsum']					,   7						)
					self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase					,   'epoch_0'						)						)						)
					self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase					,   'summarization_no_trainer'						)						)						)






			@slow
			@mock.patch.dict(os.environ					,   {'WANDB_MODE': 'offline'}						)
			def 						_lowercase  (     self						)							->							int:
					snake_case__	     =self.get_auto_remove_tmp_dir()
					snake_case__	     =f"""
            {self.examples_dir}/pytorch/translation/run_translation_no_trainer.py
            --model_name_or_path sshleifer/student_marian_en_ro_6_1
            --source_lang en
            --target_lang ro
            --train_file tests/fixtures/tests_samples/wmt16/sample.json
            --validation_file tests/fixtures/tests_samples/wmt16/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=50
            --num_warmup_steps=8
            --num_beams=6
            --learning_rate=3e-3
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --source_lang en_XX
            --target_lang ro_RO
            --checkpointing_steps epoch
            --with_tracking
        """.split()

					run_command(self._launch_args + testargs						)
					snake_case__	     =get_results(_UpperCAmelCase						)
					self.assertGreaterEqual(result['eval_bleu']					,   30						)
					self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase					,   'epoch_0'						)						)						)
					self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase					,   'translation_no_trainer'						)						)						)






			@slow
			def 						_lowercase  (     self						)							->							List[Any]:
					snake_case__	     =logging.StreamHandler(sys.stdout						)
					logger.addHandler(_UpperCAmelCase						)

					snake_case__	     =self.get_auto_remove_tmp_dir()
					snake_case__	     =f"""
            {self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py
            --dataset_name huggingface/semantic-segmentation-test-sample
            --output_dir {tmp_dir}
            --max_train_steps=10
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
        """.split()

					run_command(self._launch_args + testargs						)
					snake_case__	     =get_results(_UpperCAmelCase						)
					self.assertGreaterEqual(result['eval_overall_accuracy']					,   0.10						)






			@mock.patch.dict(os.environ					,   {'WANDB_MODE': 'offline'}						)
			def 						_lowercase  (     self						)							->							Union[str, Any]:
					snake_case__	     =self.get_auto_remove_tmp_dir()
					snake_case__	     =f"""
            {self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py
            --model_name_or_path google/vit-base-patch16-224-in21k
            --dataset_name hf-internal-testing/cats_vs_dogs_sample
            --learning_rate 1e-4
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 1
            --max_train_steps 2
            --train_val_split 0.1
            --seed 42
            --output_dir {tmp_dir}
            --with_tracking
            --checkpointing_steps 1
        """.split()

					if is_cuda_and_apex_available():
							testargs.append('--fp16'						)

					run_command(self._launch_args + testargs						)
					snake_case__	     =get_results(_UpperCAmelCase						)
					# The base model scores a 25%
					self.assertGreaterEqual(result['eval_accuracy']					,   0.6						)
					self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase					,   'step_1'						)						)						)
					self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase					,   'image_classification_no_trainer'						)						)						)









'''simple docstring'''

from collections import OrderedDict
from typing import List, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__     :						Union[str, Any]	=  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__     :						Union[str, Any]	=  {
    '''google/efficientnet-b7''': '''https://huggingface.co/google/efficientnet-b7/resolve/main/config.json''',
}

class 							a__( snake_case__     ):
			a_	: int									=      '''efficientnet'''






			def __init__(     self					,   _UpperCAmelCase = 3					,   _UpperCAmelCase = 600					,   _UpperCAmelCase = 2.0					,   _UpperCAmelCase = 3.1					,   _UpperCAmelCase = 8					,   _UpperCAmelCase = [3, 3, 5, 3, 5, 5, 3]					,   _UpperCAmelCase = [32, 16, 24, 40, 80, 112, 192]					,   _UpperCAmelCase = [16, 24, 40, 80, 112, 192, 320]					,   _UpperCAmelCase = []					,   _UpperCAmelCase = [1, 2, 2, 2, 1, 2, 1]					,   _UpperCAmelCase = [1, 2, 2, 3, 3, 4, 1]					,   _UpperCAmelCase = [1, 6, 6, 6, 6, 6, 6]					,   _UpperCAmelCase = 0.25					,   _UpperCAmelCase = "swish"					,   _UpperCAmelCase = 2560					,   _UpperCAmelCase = "mean"					,   _UpperCAmelCase = 0.02					,   _UpperCAmelCase = 0.001					,   _UpperCAmelCase = 0.99					,   _UpperCAmelCase = 0.5					,   _UpperCAmelCase = 0.2					,   **_UpperCAmelCase					,   )							->							Dict:
					super().__init__(**_UpperCAmelCase						)

					snake_case__	     =num_channels
					snake_case__	     =image_size
					snake_case__	     =width_coefficient
					snake_case__	     =depth_coefficient
					snake_case__	     =depth_divisor
					snake_case__	     =kernel_sizes
					snake_case__	     =in_channels
					snake_case__	     =out_channels
					snake_case__	     =depthwise_padding
					snake_case__	     =strides
					snake_case__	     =num_block_repeats
					snake_case__	     =expand_ratios
					snake_case__	     =squeeze_expansion_ratio
					snake_case__	     =hidden_act
					snake_case__	     =hidden_dim
					snake_case__	     =pooling_type
					snake_case__	     =initializer_range
					snake_case__	     =batch_norm_eps
					snake_case__	     =batch_norm_momentum
					snake_case__	     =dropout_rate
					snake_case__	     =drop_connect_rate
					snake_case__	     =sum(_UpperCAmelCase						) * 4





class 							a__( snake_case__     ):
			a_	: List[str]									=      version.parse('''1.11'''     )






			@property
			def 						_lowercase  (     self						)							->							Mapping[str, Mapping[int, str]]:
					return OrderedDict(
					    [
					        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
					    ]						)






			@property
			def 						_lowercase  (     self						)							->							float:
					return 1E-5




